Aulia Pradipta Luthani
University of Glasgow, Skotlandia
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INFO ARTIKEL |
ABSTRACT |
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Keywords: Feasibility;
Diversification Strategies; Coal Mining Companies. |
Following the global commitment on net zero
emissions during the Paris Agreement in 2016, Indonesia is fulfilling its
commitment by focusing their strategy on electric vehicles batteries
investment and decreasing the coal-based power generation. One of the company
groups in Indonesia that mainly operating on coal starts to look at green
minerals that would increase in demand as a diversification strategy. This
study aims to investigate which green mineral options that would be the most
feasible strategy for the company through decision-making method. Based on
the literature review on business strategy and decision-making method
decision tree analysis, a questionnaire was distributed across the internal
team of the target company that works on the diversification attempt. The
respondents were asked on their confidence on the proposed green mineral
options. Analysis on the responses shows that there is a strong, if not
perfect, relationship between the confidence scores and all the calculated
expected monetary values (EMV) of the proposed green minerals. The analysis
also shows that the highest EMV compared to other green mineral option, is
gold. On this basis, it is recommended for the company to choose gold as
their diversification strategy on green minerals. Further research is needed
to investigate other factors that has not been covered in this dissertation,
or to investigate through other decision-making method. |
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The 2016 Paris Agreement is a international treaty on climate
change that has been enforced in November 2016. The goal of the treaty is to
hold the rising global temperature into 20 Celsius above
pre-industrial levels. Furthermore, the goal is also to pursue even further the
limit of global temperature into 1.50 Celsius above pre-industrial
levels
The 2016 Paris Agreement, which 185 parties
have ratified, is aimed to combat the climate change such as the accumulation
of greenhouse gas emissions. This climate change has global implications, such
as higher global temperatures, rising sea levels, increasing variable rainfall
affecting people and the environment
Indonesia is attempting to fulfil its net
zero emission commitment can be concluded by its electric vehicle and battery
production focus
The global pressure to countries to meet the
2016 Paris Agreement and the increasing interest in electric vehicles has
increased the global demand for mineral commodities, such as Nickel and
Aluminium. On the other hand, Coal-based power plants in Indonesia are
pressured to be decreased due to their tax on the environment
With the objective of Indonesia in fulfilling
the 2016 Paris Agreement in mind, one of the company groups in Indonesia aims
to take this opportunity to steadily shifting from coal mining activities by
building a diversification strategy to green minerals as the first step.
The aim of this dissertation is to find out
the feasibility of diversification strategies for a company with a long history
of coal mining activity, which is choosing the most appropriate, best
prospective green minerals mining to venture into.
The research aims to answer the following
questions: 1. To investigate the advantages and disadvantages of green mineral
mining options that TC Group is capable of entering. 2. To investigate the most
feasible mineral for TC Group through the decision-making analysis as the
diversification strategy. 3To investigate the relationship between the
confidence of TC Group internal team to the expected monetary value on each
mineral. 4 To know the advantages and disadvantages of the used decision-making
method in this dissertation.
Many existing literatures discussed the feasibility
of establishing a certain mineral mining operation in Indonesia. But not many
literatures discussed establishing a mineral mining operation as a group
company whose mining operation is heavily reliant on coal.
Furthermore, there is not much literature investigating using decision
tree as a methodology through the experts� perception on business strategy, let
alone in a mining industry. There are, however, literatures on similar field
like the decision-making factors exploration, for example, on energy efficiency
projects
The term research may be
traced back to the French word �recherch� which means to survey
From this research may be
defined as an activity through the use of scientific and systematic approaches
with a purpose to gain a valuable information and understanding of specific
area of topic, thereby increasing their knowledge
Research methodology is a
strategy on how the research will be conducted. It is ideally follows believes,
philosophical, and paradigms surrounding on the said research, which helps said
research to chosen research techniques and understanding keep consistent and
relevant
Research philosophy itself
is a system of belief that may be used to develop knowledge. The construction
of research methodologies may be aided by the understanding of research onion
proposed by Saunders et al. (2019), which philosophy of research may be
constructed by layers upon layers, similar to onion.
Figure 0.1. Research onion diagram
The research onion is
theoretical concept that constructed research philosophy as the outermost
layer, followed by research approach, methodological choice, research strategy,
time horizon, and techniques and procedures.
Following the construct of
layers, this dissertation has chosen the pragmatism as research philosophy.
Pragmatic approach is a philosophy where the research is conducted through
method or methods that is best on solving the problem. Pragmatic researcher ignores
the debate or discussion on the best method, because pragmatic research
realizes each methods have their own unique strength and weaknesses
Pragmatic research
emphasizes on the practical methods. This is supported by the fact that the
nature of this research is to give insights and second opinion to firm that in
the process of their new investment. Furthermore, one of the aims of this
research is to find out how the process on this decision is based on a
decision-making framework of decision trees.
On to the second layer of
research onion, approach to the theory development, this dissertation has
chosen on the abductive approach. Instead of going from theory to data like
deduction approach, or the data to theory like inductive approach, the abductive
approach will be the most suitable for this dissertation as it is going back
and forth between deductive and inductive. This is supported by the fact that
Indonesia is aiming for the EV batteries production that works as an early
premises for this dissertation.
Furthermore, abductive
approach is valued for this reason in management researcher
On the third layer of
research onion, the mono method quantitative will be chosen as the decision
tree is the exclusive chosen method for this dissertation as it is simple to
use with the appropriate data, have a good accuracy with the probability and uncertainty
of the outcome accounted for
The fourth layer of
research onion, which is the strategy in which the chosen strategy is conducted
through a survey. Survey research may employ large or small population that
will be questioned about certain events. This nature of the strategy will give
an idea on complex problems into statistical and non-statistical insights to
get a generalization out of those insights
The fifth layer of research
onion, which is time horizon, the cross-sectional will be chosen. This is by
the fact that the nature of the survey itself is cross-sectional
Furthermore,
cross-sectional study does not suffer from time and mortality rate.
Cross-sectional study offers merit of the possibility of solution with the time
constraint, parallel with the very nature of this dissertation. Since the
nature of this dissertation focuses on the best strategy with the current state
of the firm in mind, choosing longitudinal research might prove to be an
irrelevant strategy
Result
Characteristics
of the Respondents
A questionnaire has been handed out to the internal members across TC
Group and AIM Company. Each internal member is regarded as an expert in their
field, which in this case, is the mining industry. Each of these members have a
varying result on their view of each mineral mining, thus resulting in a
varying result of the data. First, we look at the characteristics of the
respondents.
|
Respondent ID |
Gender* |
Age Group** |
Employment Level*** |
|
A |
1 |
2 |
2 |
|
B |
1 |
3 |
2 |
|
C |
1 |
2 |
2 |
|
D |
1 |
1 |
1 |
|
E |
1 |
3 |
2 |
|
F |
1 |
4 |
4 |
Table A. Charasteristics
of Respondents
Each respondent is categorized by their gender assigned at birth, age
group, and employment level. This is so to see if there are any differences
among the respondents, for example experiences, that may have played a role in
their subjective view to explore. While each respondent�s gender, age group,
and employment level are marked by the number codes as seen as below:
Table B. Code numbers on charasteristics
of respondents
The research managed to obtain six respondents from the firm. From the
characteristics of the respondents, the respondents are all identifies as a
male. Out of six respondents, one respondent is in the first age group, which
is the 22-32 age group. Two respondents are in the second age group, which is
the 32-42 age group. Two respondents are in the third age group, which is 32-42
age group. Lastly, one respondent in the fourth age group, which is more than
52 years old.
From the perspective of the employment level, out of six respondents,
four of them are in the managerial position. One respondent is in the position
below managerial level, which is the level of superintendent and below.
The research also aimed to obtain the respondent at the level of the
executive officers, but the author was unable to obtain the respondent. The
research also aimed to obtain the respondent of the external officers, for
example, the consulting firm that the firm works with in their feasibility
strategy phase. However, the author is also unable to obtain the respondent of
the stakeholders.
While the dissertation is unable to obtain the respondent of the level
of the executives and external stakeholders, the dissertation managed to obtain
the respondent at the level of the board of directors or shareholders, which is
the respondent F, that serves as the minor shareholder and previously worked at
the firm as an advisory level before retiring recently. With that qualification
and experiences, the author deemed the respondent F qualified to become the
respondent of this dissertation.
Answers
of the Respondents
This dissertation investigates the subjective view of the mining
experts in the TC Group and AIM Company on mineral mining. Each respondent
answered five questions on the statements on each mineral options that are
given in the questionnaire. Each question has answering scale of one-to-five,
with five being the highest favorable answer for the statements given and one
being the lowest favorable answer for the statements given. These answers given
by the respondents are being averaged to each mineral options, like the figure
below:
Figure 1 Confidence scores
of the respondents
From the chart above, all the respondents have answered that the
mineral options, which is silica, gold, and mineral to be favorable. This is
due to the fact that all the average scores of the respondents� confidence on
all mineral options scores more than three. Furthermore, all the answers
submitted by the respondents showed that there are no answers less than three
on the scale.
All the responses gathered by the respondents also showed that the
mineral gold may prove to be the mineral with most confidence, as three
respondents answered that the confidence on mineral gold is higher compared to
two other minerals, showed in the answer results of the respondent C,
respondent D, and respondent E.
Furthermore, two respondents answered that the confidence on mineral
gold is high as the confidence on silica, as answered by the respondent A and
respondent B, while the confidence on nickel answered by these two respondents
are lower.
On the contrary, respondent F answered slightly differ than the rest of
the five respondents. Respondent F answered that the confidence on mineral gold
is the lowest compared to the other two mineral options.
From the focus on the nickel mineral options, the nickel mineral
options may prove to be the the mineral options with
the least confidence. Out of six respondents, four of the respondents answer
results shows that the nickel is the lowest confidence compared to the other
two minerals, as showed in the chart answer results of respondents A, respondent
B, respondent D, and respondent E. respondent C also shows that the nickel is
the lowest mineral options with silica mineral options, as low as 3.80 in the
confidence score.
As mentioned in the previous sub-chapter, the respondents� confidence
on each mineral have different subjective view on the
matter. The decision tree can be calculated and constructed by the confidence
on each mineral option of each respondent.
Another variable to construct the decision tree is the payoff, which
will be taken from the answers of the questionnaire on the NPV of each mineral
options. All the responses of the respondents on the NPV of each mineral
options are uniform, which are $35 million, $190 million, and $69 million, for
silica, gold, and nickel, respectively.
Additional variable to construct to the decision tree of this research
is to include the differences of good condition and bad condition of the
projected mining activities, should they venture into specific mineral options.
This is able to be obtained by the respondents answer of the projected
production when the company is in good condition and bad condition, on each
mineral, in a single year. Each respondent answers on the productions are
uniform across the respondent. Each respondents answered the productions on the
good and bad conditions are: 1.5 million Mt and 0,6 million Mt, respectively
for mineral silica; 100,000 ounces and 50,000 ounces, respectively for mineral
gold; and 20,000 Ton Ni and 14,000 Ton Ni, respectively for mineral nickel.
These projected productions are assumed to be proportional to the NPV. With the
information above, we can construct the decision tree of each respondent as
follows:
Figure 2 Constructed
decision tree on Respondent A
Figure 3 Constructed
decision tree on Respondent B
Figure 0 Constructed decision tree on Respondent C
Figure 5 Constructed
decision tree on Respondent D
Figure 6 Constructed
decision tree on Respondent E
Figure 7 Constructed
decision tree on Respondent F
All the decision trees in the figure above are constructed through the
online software Silver Decisions. As mentioned in the previous chapter of
literature review, expected monetary value (EMV) is calculated by the
probability of each option and their respective payoffs.
In this research, the probability of each option is taken from the
expert confidence on each mineral option, and the payoffs in the decision tree
are taken from the expected NPV or net present value of each mineral option
taken from the answers of the respondent in the questionnaire. Thus, in this
research, the expert�s confidence on each mineral options to their respective
NPVs are used as the calculation basis.
Since there are both EMV on assumed good condition and bad condition on
each mineral options (which denoted as high and low, respectively in the
figure), both EMVs are summed to get the weighted EMVs of each mineral options.
The good condition payoffs are assumed to be the NPV of each mineral option,
while the bad condition payoffs are taken from the ratio of the good and bad
projected production of each mineral. Then both EMVs are determined from which
out of these EMVs has the highest amount as the most feasible strategy.
As can be seen by the constructed decision trees, the highest EMV of
three mineral options out of each respondent showed to be the mineral gold
option. The highest EMV of mineral option gold is shown by the respondent D,
with the amount reaching $184 million. Respondent A, respondent B, and
respondent D showed the second highest amount of mineral gold EMV with the
amount of $178 million. Respondent E showed the second lowest amount of gold
EMV with the amount of $174 million. Respondent F showed the lowest amount of
mineral gold EMV compared to other respondents, with as much as $167 million.
On the contrary, the lowest amount of EMV out of three mineral options
across the respondent showed to be the silica mineral option. The lowest EMV of
silica mineral options are shown by the confidence results of the respondent C
and respondent E, with the amount as low as $30 million. Followed by the
confidence result of respondent D, with the amount of $31 million. The
confidence result of respondent A, respondent B, and respondent F shows the
highest EMV of silica mineral options, with the amount of $32 million.
While mineral gold options and mineral silica showed as the highest and
the lowest EMV value among the respondent, the mineral nickel options show as
the middle value between the other two minerals options. The lowest EMV of
nickel was shown to be the result of the respondent A and respondent D, with
the EMV amount of $63 million. While the highest EMV of nickel is shown by the
result of the respondent B and respondent F.
Following the decision tree analysis done in the previous sub-chapter,
this dissertation also carried out the regression analysis on the independent
variable and the dependent variable used in this research. The regression
analysis in this research is used to see the relationship between the dependent
variable and the independent variable, which how close both variables are.
As mentioned in the previous chapter, the dependent variable in this
research is the EMV of the respective mineral options. On the other hand, the
independent variable in this research is the respondents� confidence in the
respective mineral options.
The regression analysis table is constructed through the software of
Microsoft Excel, with the regression result for the variables are shown below.
Three regression analyses were done in this research, for each mineral option.
There are three tables constructed, the first table shows the regression
statistics, the second table shows the significance of the data, and the third
table shows the coefficient of two variables.
The regression statistics table shows the fit of the data, which
denoted by the letter R. This shows the fit or the strength of the linear
relationship between two variables, also known as the Pearson correlation
coefficient value, with the range value of negative 1 to positive 1. The value
of negative 1 show that the variables have total negative correlation, the
value of 0 shows no correlation, and the value of positive 1 shows the total
positive correlation
The second table shows analysis of variance (ANOVA). It shows the
significance of the data and denoted on the table as �significance F�. This
value shows the reliability of the hypotheses and helps in the decision whether
a hypothesis has relationship between two variables or not. The hypothesis is
accepted as having linear relationship between two variables when the value is
less then 0.05, and no linear relationship is
accepted when the value is more than 0.05.
The third table shows the coefficient table, which is to help draw the
linear equation between the independent variables and the dependent variables.
Where there are the intercept value, and the
independent variables of experts confidence
Table 3 Regression
statistics on mineral option silica
Table 4 Regression
statistics on mineral option gold
Table 5 Regression
statistics on mineral option nickel
�� This chapter serves as the
continuation of the previous chapter, which is to discuss further the findings
mentioned. This chapter also discusses whether the hypotheses mentioned in the
second chapter are accepted or not based on the discussion in the fourth
chapter.
As also mentioned in the second chapter, there are two types of
hypotheses generated in the conceptual framework and hypotheses sub-chapter,
which the first type of hypotheses is whether there is relationship between the
independent variables and the dependent variables on the mineral silica,
mineral gold, and mineral nickel.
The second type of hypotheses is whether one of mineral options will be
deemed as the most feasible strategy through the analysis of the decision trees
done in the previous chapter of analysis and findings.
Confidence on EMV Hypothesis
Discussions
As shown in the regression statistics table of the silica, the Pearson
correlation coefficient, or denoted in the table as �Multiple R�, shows that
the value is 1. A value approaching 1 is considered as a strong, if not perfect
relationship between the variables. Thus, the value of 1 in regression
statistic of mineral option silica indicates strong strength, a 100%
correlation between the variables.
Furthermore, if we look at the coefficient of determination, denoted as
R2, is calculated by square the value of R. Thus, the coefficient
value is the range between 0 and 1. The value of 0 indicates that the
independent variable is not correlated with the dependent variable. On the
contrary, the value given in the regression statistic table of the mineral
option silica is 1. It indicates that the value is perfect fit. Theoretically,
the value of 1 also indicates the independent variable allows errorless
prediction on the dependent variable.
On the second table, which is the ANOVA table, the significance of the
data, which is the probability of the regression data model is wrong, may prove
that the hypothesis is accepted or not whether the value of the Significance F
is lower than 0.05 (the confidence is set at 95%). The value is significantly
low, to the extent of 1.2 to the power of negative 61, making it have extremely
low probability.
From the analysis and discussion done above, it can be concluded that
the independent variable of expert confidence on silica is having a linear
relationship with the dependent variable of expected monetary value of silica.
Thus, the hypothesis H1 is accepted.
Hypothesis H2 is formulated similarly to hypothesis H1,
which is to investigate whether there is a positive linear relationship between
the experts� confidence in silica and the expected monetary value on silica.
However, hypothesis 2 investigates the same relationship on mineral gold. The
EMV of the mineral option silica serves as the dependent variable and the
respondents confidence score serves as the independent variable.
As shown in the regression statistics table of the mineral gold, the
Pearson correlation coefficient, also shows that the value is 1. A value
approaching 1 is considered as a strong relationship between the variables.
Thus, the value of 1 in regression statistic of mineral option gold indicates
strong strength relationship between the variables.
If we look at the coefficient of determination, the value of
coefficient of determination of mineral gold option is1. It indicates that the
value is perfect fit, like the silica option. Theoretically, the value of 1
also indicates the independent variable allows errorless prediction on the
dependent variable.
Moving on to the ANOVA table, the significance of the data, which is
the probability of the regression data model is wrong, may prove that the
hypothesis is accepted or not whether the value of the Significance F is lower
than 0.05. The value of the Significance F is 1.6 to the power of negative 61,
making it the probability of data being incorrect is extremely low.
Focusing on the regression equation table, we can look at the two
important things on the table. First, we can look at the coefficient of the
confidence (the independent variable), which the value is positive, also
indicates a positive relationship to the dependent variable. We can also see
that the P-value is 1 to the power of negative 60, extremely lower than the
0.05 from the 95% confidence set.
From the analysis and discussion done above, it can be concluded that
the independent variable of expert confidence on gold has a strong linear
relationship with the dependent variable of expected monetary value of gold.
Thus, the hypothesis H2 is accepted.
Hypothesis H3, similar to hypothesis H1 and H2,
is to investigate whether there is a positive linear relationship between the
expert�s confidence in nickel and the expected monetary value on nickel. The H3
uses the EMV of the mineral option silica serves as the dependent variable and
the respondents confidence score serves as the independent variable.
First, we look at the regression statistics table of the mineral option
nickel, the Pearson correlation coefficient also shows that the value is 1,
same value as the Pearson correlation coefficient value of mineral silica and
mineral gold. A value approaching 1 is considered as a strong relationship
between the variables. Thus, the value of 1 in regression statistic of mineral
option nickel indicates 100% correlation on the dependent variables.
Moving on to the ANOVA table, the significance of the data, which is
the probability of the regression data model is wrong, may prove that the
hypothesis is accepted or not whether the value of the Significance F is lower
than 0.05. The value of the Significance F is 2.8 to the power of negative 65,
making it the probability of data being incorrect is extremely low.
Focusing on the regression equation table, we can look at the two
important things on the table. First, we can look at the coefficient of the
confidence (the independent variable), which the value is positive, also
indicates a positive relationship to the dependent variable. We can also see
that the P-value is 2.7 to the power of negative 65, lower than the 0.05 from
the 95% confidence set.
From the analysis and discussion done above, it can be concluded that
the independent variable of expert confidence on nickel has a strong linear
relationship with the dependent variable of expected monetary value of nickel.
Thus, the hypothesis H3 is accepted.
Feasible Minerals Hypothesis
Discussions
As stated in the previous chapter, the calculated EMV of each mineral
is accounted from the probability of each mineral, and the payoffs of both
hypothetical good condition and bad condition. The probability of each mineral
is taken from the respondents� confidence score, which is calculated from the
answers of the respondents on their subjective view of the silica mineral. On
the other hand, the payoffs of both hypothetical good condition and bad
condition are taken from each respondent answer on the projected production per
year, on good condition and bad condition.
There are assumptions being made in the calculation process. The first
assumption was that the projected production is assumed to be constant
throughout the lifetime of mining activity. The second assumption was that the
answer of NPV of each mineral was proportional to the projected production. The
third assumption that the expected NPV answers across the respondents was
assumed to be the good condition payoff.
Hypothesis H4 seeks to investigate whether mineral option
silica, would be the most feasible strategy. This research also investigates
alternative minerals comparatively with are mineral gold and mineral nickel.
First, we look at the constructed decision trees across six
respondents. The confidence of mineral option silica for each respondent
varied. Three out of six respondents have confidence score of 88%, while the
other three respondents have insignificantly lower confidence score of 76%,
80%, and 84%.
The projected production and the expected NPV value of mineral option
silica across the respondents are uniform, which all respondents suggest that
the projected production of mineral option silica for hypothetical good
condition and bad condition was 1.5 million Mt and 0.6 million Mt,
respectively. Furthermore, all the respondents answered $35 million in rough
approximation as the expected NPV.
The calculation of EMV of each respondent was taking the respondents�
confidence score and the payoffs of both hypothetical conditions into account.
The calculated EMV from each respondent was in the range of $30 million at the
lowest and $32 million at the highest. The value of EMV of mineral option
silica was the lowest in comparison with the other two mineral options. Thus,
through the decision tree analysis, the hypothesis H4 is rejected.
However, if we investigate other factors, such as qualitative factors,
the respondents were asked on their opinion on benefit of venturing into
mineral silica as diversification strategy through the questionnaire. The
respondents answered that mineral silica was one of the essential materials
used in the production of photovoltaic cells, or commonly known as solar
panels. This is consistent with study that shows high purity silica is
essential towards the production of photovoltaic cells
On the other hand, there are some challenges should the company venture
into silica mine. Respondents answered that silica mining is a new industry in
Indonesia, mining licenses are limited, the midstream and downstream value
chain is still underdeveloped. Another challenge was that the risk of
substitution, as one of the respondents answered that there are
risk of silica being material of photovoltaic cells is being replaced with
other materials.
Hypothesis H5 seeks to investigate whether mineral option
gold, would be the most feasible strategy. This research also investigates
alternative minerals comparatively with mineral silica and mineral nickel.
From the constructed decision trees on gold, the confidence of mineral
option gold for each respondent varied. Three out of six respondents have a
confidence score of 88%, while the other three respondents have insignificantly
lower confidence scores of 76%, 84%, and 92%.
The projected production of mineral option gold across the respondents
are uniform, which all respondents suggest that the projected production of
mineral option gold for hypothetical good condition and bad condition was
100,000 ounces and 50,000 ounces, respectively. Furthermore, the respondents�
answer on the expected NPV on mineral option gold also uniform across the
respondents, which all the respondents answered $190 million in rough
approximation.
The calculated EMV from each respondent varied, with valued at $167
million at the lowest and $182 million at the highest. The value of EMV of
mineral option gold was the highest in comparison with the other two mineral
options, across all respondents. Thus, through the decision tree analysis, the
hypothesis H5 is accepted as the most feasible mineral option.
Even though the gold mineral option has the highest value of EMV out of
all mineral options, there are some benefits and challenges. For example, the
respondents� answers on the benefit of venturing into mineral gold stated that
gold is an attractive industry, due to the market stability and the high
commodity prices.
However, the challenges of venturing into gold are not benign.
Respondents� answers cited the challenges where the opportunity of gold mine is
limited due to lack of exploration. This is supported by reports published that
gold exploration phase took many years, as much as 12-16 years
Hypothesis H6 seeks to investigate whether mineral option
nickel, would be the most feasible strategy. This research also investigates
alternative minerals comparatively with other mineral options, which are
mineral silica and mineral gold.
Focusing on the constructed decision trees across six respondents, the
confidence of mineral option nickel for each respondent varied. Among six
respondents, each pair of respondents have confidence scores on silica of 72%,
76%, and 84%.
The projected production and the expected NPV value of mineral option
nickel across the respondents are uniform, which all respondents suggest that
the projected production of mineral option silica for hypothetical good
condition and bad condition was 20,000 Ton Ni and 14,000 Ton Ni, respectively.
Furthermore, all the respondents answered that the expected NPV of nickel in
rough approximation was $69 million.
The calculated EMV from each respondent varied, with each pair
resulting in a value of $62 million at the lowest and $65 million at the
highest. The value of EMV of mineral option gold was the highest in comparison
with the other two mineral options, across all respondents.
In conclusion, through the decision tree analysis, the hypothesis H6
is rejected as the most feasible mineral option, as the highest calculated EMV
belongs to the mineral option gold.
Nickel mining as a diversification strategy poses benefits and threats.
One of the benefits of venturing into nickel mining, as the respondents
suggest, Indonesia is one of the countries with the highest nickel deposit, in
parallel with the report that Indonesia is the highest nickel extractor
according to published report
In contrast, the threat of venturing into nickel poses, comes in the
form of finding the suitable partner for nickel processing is limited. As one
answer suggests, class 1 nickel is required for EV production. Class 1 nickel
is purity grade with more than 99.8% purity
CONCLUSION
This chapter aims to
see the dissertation in full view, concluding its results and further
discussion that has not been covered in the previous chapters. This chapter
also aims to discuss the research, such as the limitations emerged on doing the
research, and recommendations on possible future research in filling the gaps
on said limitations.
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